论文标题
二进制和多类心血管疾病的严重性预测 - 一种基于机器学习的融合方法
The Severity Prediction of The Binary And Multi-Class Cardiovascular Disease -- A Machine Learning-Based Fusion Approach
论文作者
论文摘要
在当今世界，几乎每个行业都可以使用大量数据。这些数据已成为资产，因为我们可以使用大量数据来查找信息。主要是医疗保健行业包含许多由患者和疾病相关信息组成的数据。通过使用机器学习技术，我们可以寻找隐藏的数据模式来预测各种疾病。最近，CVD或心血管疾病已成为世界各地死亡的主要原因。 CVD导致的死亡人数令人恐惧。这就是为什么许多研究人员正在尽力设计一个可以使用数据挖掘模型来挽救许多生命的预测模型的原因。在这项研究中，已经构建了一些融合模型来诊断CVD及其严重性。机器学习（ML）算法（例如人工神经网络，SVM，Logistic回归，决策树，随机森林和Adaboost）已应用于心脏病数据集，以预测疾病。由于多类分类中的类不平衡而实现了Randomovers采样器。为了提高分类的性能，采用了加权得分融合方法。起初，模型进行了培训。训练后，使用加权和规则合并了两个算法的决定。从六种ML算法开发了三个融合模型。结果参数有希望。对二进制和多类分类问题的不同测试训练率进行了实验，而对于这两个训练率，融合模型都表现良好。多类别分类的最高精度为75％，二进制为95％。代码可以在：https：//github.com/hafsa-kibria/weighted_score_fusion_model_heart_disease_prediction
In today's world, a massive amount of data is available in almost every sector. This data has become an asset as we can use this enormous amount of data to find information. Mainly health care industry contains many data consisting of patient and disease-related information. By using the machine learning technique, we can look for hidden data patterns to predict various diseases. Recently CVDs, or cardiovascular disease, have become a leading cause of death around the world. The number of death due to CVDs is frightening. That is why many researchers are trying their best to design a predictive model that can save many lives using the data mining model. In this research, some fusion models have been constructed to diagnose CVDs along with its severity. Machine learning(ML) algorithms like artificial neural network, SVM, logistic regression, decision tree, random forest, and AdaBoost have been applied to the heart disease dataset to predict disease. Randomoversampler was implemented because of the class imbalance in multiclass classification. To improve the performance of classification, a weighted score fusion approach was taken. At first, the models were trained. After training, two algorithms' decision was combined using a weighted sum rule. A total of three fusion models have been developed from the six ML algorithms. The results were promising in the performance parameter. The proposed approach has been experimented with different test training ratios for binary and multiclass classification problems, and for both of them, the fusion models performed well. The highest accuracy for multiclass classification was found as 75%, and it was 95% for binary. The code can be found in : https://github.com/hafsa-kibria/Weighted_score_fusion_model_heart_disease_prediction
